Continuous human gait tracking using sEMG signals

Abstract

Gait can reflect human biological status during walking, which can be used for disease detect, identity verification or robot control, etc. Traditionally, gait analysis only classifies a gait cycle into a few discrete stages. In this paper, human gait will be decoded continuously using surface electromography (sEMG). The angle of knee joint and ankle joint during walking at different speed will be estimated at the same time by the proposed scheme. Four time domain features combined together will be used for the task. Six estimation methods will be compared and the best performance reaches the RMSE of 6.64° for knee and 3.89° for ankle. The proposed method shows great potential for the gait tracking problem.